Search Results (17,291)

Quantifying time-dependent rock mass degradation is critical for assessing long-term slope stability during open-pit mine closure. This study evaluates the geotechnical evolution of Paleoproterozoic arenites and argillites in the semi-arid Essakane Main Zone (Burkina Faso) over a 0–9-year atmospheric exposure period. Field characterization across 32 sampling stations included density measurements, point load testing (Is₍₅₀₎), determination of the Geological Strength Index (GSI), and petrographic analysis. The results demonstrate a time-dependent reduction in physico-mechanical properties, modeled with a high correlation (R² = 0.80–0.99). While density exhibited minor reductions, structural degradation was pronounced; the GSI decreased by 10 points for both lithologies, and Is₅₀ dropped significantly, particularly in argillites (4.1 to 2.3 MPa) relative to arenites (4.0 to 3.6 MPa). Petrographic evidence indicates negligible chemical weathering and mineral neoformation. Consequently, the degradation was attributed primarily to physical processes, specifically microcracking and discontinuity deterioration driven by thermal cycling and phyllosilicate sensitivity in argillites. These empirical relationships provide essential quantitative input for numerical slope stability modeling in semi-arid mine closure scenarios. Full article

Purpose: To evaluate the safety and feasibility of repeated subtenon administration of autologous platelet-rich plasma (PRP) in patients with degenerative retinal diseases and to explore preliminary, hypothesis-generating functional observations in retinitis pigmentosa (RP) and extensive macular atrophy with pseudodrusen-like appearance (EMAP). Methods: This prospective, open-label, uncontrolled pilot study included 13 patients (6 RP, 7 EMAP) who received three subtenon PRP injections (1.5 mL each) at baseline, Month 2, and Month 4, with follow-up through Month 6. The study was designed primarily to assess safety and feasibility and was not powered or intended to evaluate efficacy. The primary outcome was safety, including adverse events and intraocular pressure changes. Exploratory secondary outcomes included best-corrected visual acuity (BCVA, logMAR), visual field mean deviation (MD), and structural optical coherence tomography (OCT) parameters. Electrophysiological outcomes were analyzed descriptively due to incomplete paired data. Analyses were conducted within diagnostic groups, and no between-group comparisons were performed. Results: All 13 patients completed the study. No serious adverse events or permanent ocular morbidity were observed. Two transient and self-limited adverse events occurred (anterior uveitis and intraocular pressure elevation), both resolving without sequelae. In the overall cohort, BCVA remained stable without statistically significant change. In the RP subgroup, a small exploratory change in BCVA was observed (mean ΔlogMAR −0.09; nominal p = 0.048), corresponding to approximately 4–5 ETDRS letters; however, this finding was associated with wide confidence intervals and limited statistical power and should be interpreted cautiously. In the EMAP subgroup, functional stability was observed without evidence of consistent improvement. Visual field mean deviation and OCT findings were consistent with absence of short-term deterioration across available paired data. Electrophysiological outcomes showed no consistent directional change. Conclusions: Repeated subtenon PRP administration appeared feasible and well tolerated in this small, uncontrolled pilot cohort. Any observed functional changes are preliminary and hypothesis-generating only and do not establish efficacy. Larger, adequately powered controlled studies with standardized endpoints are required to determine the potential role of PRP in degenerative retinal diseases. Full article

The design of multitargeted drug candidates has recently emerged as a highly attractive area of research. Numerous heterometallic compounds have been developed to enhance both the biological efficacy and physicochemical properties of monometallic metallodrugs. Combining classical transition metals with established antitumor activity, such as Pt, Ru, and Au, with other metal-based fragments offers the potential to generate complex compounds with improved pharmacokinetic and pharmacodynamic profiles. Incorporating different bioactive metal cations within a single molecular framework may enhance anticancer activity through metal-specific interactions with distinct biological targets or through improved physicochemical characteristics of the resulting heteronuclear complexes. Recent studies have underscored the significant progress and promising impact of this multitargeted strategy, particularly in systems that combine ruthenium with other biologically active metal centers. This approach may enable selective biological targeting and help overcome drug resistance. This review compiles and analyzes reported ruthenium-based heteronuclear complexes, offering a comprehensive and critical assessment of recent advances in the rational design and synthesis of novel multinuclear compounds as potential chemotherapeutic agents. Particular emphasis is placed on understanding structure–activity relationships, mechanistic pathways, and the role of metal–metal and metal–ligand interactions in modulating biological responses. The findings summarized herein highlight the remarkable efficacy of a wide range of multinuclear ruthenium anticancer complexes and support the hypothesis that synergistic and/or cooperative interactions between distinct metal-based fragments can significantly enhance pharmacological performance, including improved selectivity, stability, and cellular uptake. Furthermore, emerging insights into their modes of action, resistance profiles, and potential for targeted delivery underscore their promise as viable alternatives to conventional therapies. Overall, this dynamic and rapidly evolving field is poised to inspire continued interdisciplinary research and drive the development of next-generation metallodrugs with improved therapeutic indices and clinical potential. Full article

Background/Objectives: Osteonecrosis of the femoral head (ONFH) is difficult to diagnose, particularly in the early stages, because radiological findings may be subtle. Delayed or inaccurate staging may increase the risk of femoral head collapse and functional loss. Although magnetic resonance imaging is highly sensitive for early-stage lesion detection, interpretation may vary depending on observer experience. Therefore, reliable and explainable automated decision support approaches are needed. Methods: In this study, a deep learning-based approach was proposed to classify ONFH into early and late stages according to the Ficat–Arlet staging system. Stage I–II cases were defined as early-stage, whereas Stage III–IV cases were defined as late-stage. Axial and coronal MR images were evaluated separately to investigate plane-dependent classification performance. The images were converted into a three-channel format, resized to a common spatial resolution, normalized, and augmented during training. Feature extraction was performed using transfer learning with modern convolutional neural network architectures. ConvNeXt Tiny was used as the main classification backbone. Weighted loss was applied to reduce the effect of class imbalance, and the decision threshold was optimized on validation data to reduce missed clinically critical late-stage cases. Results: A dataset collected from the Orthopedics and Traumatology Department of Firat University Hospital was used in the experimental evaluation. The dataset was divided into training and test sets using an 80:20 split, and 10-fold cross-validation was additionally performed to assess model stability. In the hold-out test, the axial plane model achieved 94.51% accuracy, 96.80% sensitivity, 93.49% specificity, 0.9162 F1-score, and 0.981 AUC. In the coronal plane model, 92.84% accuracy, 96.13% sensitivity, 90.96% specificity, 0.9072 F1-score, and 0.988 AUC were obtained. The 10-fold cross-validation results provided a more conservative estimate of generalization performance. Conclusions: The findings indicate that deep learning-based plane-wise analysis of MR images can distinguish early- and late-stage ONFH with high performance. Grad-CAM-based visual explanations showed that the model focused mainly on clinically relevant subchondral and weight-bearing regions of the femoral head. The proposed approach may serve as an explainable decision support tool for reducing observer-dependent variability in clinical staging. Future studies should validate the method using external, multicenter datasets and paired patient-level axial–coronal images. Full article

This paper investigates the dynamic behavior of non-Darcy seepage systems in post-failure rock. A one-dimensional non-Darcy seepage evolution equation is established, and a 6-dimensional nonlinear ordinary differential system is derived via the spectral truncation method. Eigenvalue analysis is adopted to determine the instability and bifurcation conditions, with the bifurcation diagram plotted. The fourth-order Runge–Kutta method is used to obtain phase trajectory patterns under different initial values. The results confirm the existence of transcritical bifurcations and fold bifurcations. The dynamic response of the system is discontinuous with control parameters, and phase trajectory symmetry breaking occurs with the increase in nonlinear terms. The reduced-order model shows diverse phase trajectories including equilibrium, periodic, chaotic attractors and unstable states. The system is sensitive to initial values, which significantly affect phase trajectory behaviors. The system may lose stability and trigger water inrush hazards under critical conditions. The bifurcation diagram and critical parameters obtained can provide a theoretical basis for the early warning, risk assessment and prevention of coal mine water inrush hazards. Full article

Purpose: Natural polysaccharides have shown considerable potential in the management of type 2 diabetes mellitus (T2DM) due to their multi-target metabolic regulatory effects. However, their clinical translation is limited by poor oral stability and low intestinal permeability. Snow lotus polysaccharide (SIP), a representative plant-derived polysaccharide, exhibits promising metabolic benefits but suffers from these delivery barriers. This study aimed to develop an oral nanodelivery system to enhance the gastrointestinal stability and intestinal transport of SIP, thereby improving its in vivo efficacy. Methods: SIP-loaded chitosan–tripolyphosphate nanoparticles (SIP@CS-TPP) were prepared via ionic crosslinking and characterized in terms of particle size, surface charge, morphology, and structural features. In vitro release behavior under simulated gastrointestinal conditions was evaluated. Ex vivo intestinal permeation was assessed using an isolated intestinal sac model. The metabolic regulatory effects were further investigated in a high-fat diet/streptozotocin-induced T2DM rat model. Results: SIP@CS-TPP nanoparticles exhibited a uniform particle size of 188.9 ± 12.8 nm, a surface charge of 28.3 ± 5.1 mV, and good stability after freeze-drying. A pH-responsive and diffusion-controlled release profile was observed. Ex vivo studies demonstrated significantly enhanced intestinal transport, with an approximately 3.7-fold increase in apparent permeability compared with free SIP. In vivo, SIP@CS-TPP improved glycemic control, glucose tolerance, insulin resistance, lipid metabolism, oxidative stress, and inflammatory responses more effectively than free SIP at the same dose. Conclusions: The CS-TPP nanodelivery system effectively enhances the oral delivery and metabolic regulatory effects of SIP. This study highlights the potential of a delivery-oriented strategy to improve the in vivo performance of natural polysaccharides and provides a promising approach for their application in metabolic disease management. Full article

With the fast penetration of renewable energy resources (RERs) in modern power grids, system inertia is gradually decreasing, whereby threatening frequency stability. Grid-forming (GFM) permanent magnet synchronous generator (PMSG) wind turbines have emerged as a promising solution for supporting and maintaining power system stability. Nevertheless, many studies neglect the inherent intermittency and limited power capability of RERs. As a result, the dynamic interactions between machine-side and grid-side converters are often neglected, and the DC link is commonly modeled as either an ideal voltage source or a controlled current source, which may lead to inaccurate representations of system dynamics. As a solution, this paper investigates the influence of RER intermittency and power constraints on DC-link dynamics and their impact on the frequency support performance of GFM PMSGs. First, the overall system is configured using back-to-back voltage source converters, and the system’s dynamic equations are presented. Afterwards, the impact of wind speed variations is thoroughly discussed, alongside a critical examination of the requirements specified in IEEE Standard 2800-2022. Furthermore, a supervisory curtailment strategy is proposed to ensure overall system stability under severe load disturbances when the PMSG is unable to supply the required power. Finally, detailed case studies are conducted to: (1) assess the influence of variable wind speed and DC-link voltage control on the dynamic response of PMSGs, and (2) compare the performance of the accurate DC-link dynamic model with conventional idealized and simplified models. Full article

Background/Objectives: Accurate kidney segmentation from magnetic resonance imaging (MRI) in kidney-transplant patients is essential for quantitative graft assessment, yet it remains challenging due to low tissue contrast, intensity inhomogeneity, and inter-patient anatomical variability introduced by surgical graft placement. Methods: We propose a 3D adversarial segmentation framework that incorporates probabilistic appearance and anatomical shape priors into a residual conditional generative adversarial network (GAN). The framework integrates image-driven and prior-guided information to improve boundary delineation under challenging imaging conditions and is evaluated on 100 kidney-transplant patients across T2-weighted imaging, BOLD-MRI, and DW-MRI using leave-one-out cross-validation. Results: The proposed method achieves mean Dice scores of 90.86% on T2-weighted imaging, 92.02% on BOLD-MRI, and 94.00% on DW-MRI. Consistent performance across all modalities demonstrates robustness under heterogeneous MRI acquisitions. The incorporation of prior guidance improves segmentation stability and anatomical consistency, particularly in low-contrast modalities. Conclusions: The proposed framework enables reliable kidney delineation across multiple MRI sequences, supporting consistent extraction of quantitative imaging biomarkers. This capability facilitates noninvasive assessment of renal graft function and supports longitudinal monitoring of transplant patients. Full article

Objective: Myocardial infarction (MI) triggers inflammation and fibrosis that drive the progressive impairment of cardiac function. Yet most pharmacological studies still depend on single-time-point histological or imaging endpoints and lack longitudinal, non-invasive assessments of treatment response. Electrocardiography (ECG) detects conduction and repolarization abnormalities tightly associated with myocardial injury and structural remodeling. However, ECG monitoring in mice is limited by rigid or invasive hardware, which restricts its use for longitudinal assessment of cardiac structure and function. Approach: Here, we propose an ECG-based non-invasive post-MI cardiac remodeling assessment approach and develop a flexible electrocardiographic monitoring microsystem (FECMS). Using the anti-remodeling drug (colchicine) therapy in an MI mouse model (Sham n = 4, MI n = 7 survivors, Col n = 7 survivors) for validation, we longitudinally track drug-induced changes in ECG parameters and systematically evaluate their concordance with functional, structural, and molecular indicators of cardiac injury and remodeling. Results: Colchicine treatment induced progressive shortening of the QRS and QT intervals and gradual stabilization of the PR interval. These interval changes were accompanied by increased EF and FS, decreased LVESV, reduced myocardial fibrosis and inflammatory infiltration, and lower plasma troponin I levels at the endpoint. Correlation analyses revealed strong relationships between drug-induced changes in ECG parameters and functional recovery and inhibited structural remodeling. Significance: The FECMS provides a new, non-invasive tool for longitudinal cardiovascular drug evaluation. This approach has the potential to complement or reduce reliance on terminal histological endpoints and to facilitate the optimization of dosing strategies in preclinical cardiovascular pharmacology. Full article

Expansive clay slopes are vulnerable to progressive strength loss induced by repeated wetting and drying, a mechanism that drives shallow failure in active moisture zones. Reproducing this degradation experimentally is time-consuming and resource-intensive. This study evaluates whether Fully Softened Strength (FSS) can serve as a practical substitute for five wet–dry cycles in expansive clay slope stability assessment. Direct shear tests were conducted on wet–dry-cycled and reconstituted FSS specimens across fourteen experimental water contents. Strength parameters were incorporated into homogeneous and heterogeneous limit equilibrium slope models, considering degraded layer thicknesses of 1–5 m and suspended water table conditions. Equivalence was assessed using root mean square error (RMSE), prediction bias, and physical representativeness. Five wet–dry cycles produced a dominant cohesion reduction of 70.4% with minor changes in friction angle, reaching a quasi-stationary degraded state. FSS reproduced an equivalent system response through mechanical compensation between cohesion and friction—not through equality of strength parameters—under shallow failure conditions. The best statistical fit was obtained at w = 43.5% (RMSE = 0.314); however, w = 42.0%, coinciding with the liquid limit, provided a physically more robust interpretation with near-zero bias. Equivalence was found to be valid only for normal stresses ≤ 50 kPa, representative of shallow failure depths of 1–4 m. Full article

This study presents an extended cubic B-spline Galerkin scheme for the numerical solution of multi-term time-fractional differential equations. The proposed formulation employs extended cubic B-splines together with the Caputo fractional derivative to model the time-fractional operators. Gauss quadrature is used to accurately evaluate the resulting integral. A stability analysis of the scheme is provided and its accuracy is assessed through $L_2$ and $L_{\infty }$ error norms over different spatial nodes and mesh refinements. The numerical results demonstrate excellent agreement with the exact solutions, as illustrated in the tables and figures. These findings confirm the robustness, efficiency and reliability of the proposed method for solving multi-term time-fractional differential equations. Full article

The rapid and accurate assessment of residual ultimate strength after ship damage is crucial for rescue decision-making and navigation safety, while traditional methods struggle to meet the demands of complex random damage scenarios in terms of efficiency or accuracy. This study proposes a hybrid framework that integrates high-fidelity nonlinear finite element simulation (NFEM) and a Bayesian-regularized backpropagation neural network (BPNN). NFEM is used to accurately simulate a large number of random damage scenarios, generating a physically credible benchmark dataset. BPNN serves as an efficient surrogate prediction model, with its key parameters—the number of hidden layers and the training algorithm—systematically optimized to enhance generalization capability. The results show that: (1) The NFEM simulation results deviate by less than 5% compared to the Smith method, validating the reliability of the dataset. (2) The prediction performance of BPNN is highly dependent on the number of hidden layers and the training algorithm, exhibiting non-monotonic variation, with an optimal parameter combination identified as 8 hidden layers paired with the Bayesian algorithm, achieving a prediction regression value R of 0.91662. (3) Deep networks are prone to overfitting, while shallow networks suffer from insufficient feature capture. (4) The Bayesian algorithm performs best in terms of overfitting resistance and stability. This study not only provides a high-precision and efficient intelligent solution for residual strength assessment of damaged hulls, but its systematic neural network parameter optimization strategy, particularly the approach of identifying optimal depth and selecting anti-overfitting algorithms, also offers an important reference for the design of intelligent damage assessment models for similar engineering structures. Full article

Ground penetrating radar (GPR), as an efficient non-destructive testing technique, plays a crucial role in the structural condition assessment and defect identification of railway ballast. Typical defects such as mud pumping generally exhibit characteristics in B-scan images including weak reflections, blurred boundaries, and irregular structures, which pose significant challenges for stable detection and precise localization using existing methods that rely primarily on spatial feature modeling. Most current deep learning approaches focus on modeling spatial or temporal information, while lacking effective utilization of frequency-domain features, thereby limiting their discriminative capability under complex electromagnetic environments. To address these issues, this paper proposes a single-stage object detection framework, termed YOLO-DGW, based on time-frequency collaborative modeling. Built upon YOLOv8, the proposed method introduces a structure-aware spatial enhancement module to improve the representation of continuous GPR echo structures. Meanwhile, frequency-domain information is incorporated as a modulation prior to guide spatial feature learning, enhancing the model’s sensitivity to weak reflections and complex-shaped targets. In addition, A-CIoU loss function is designed to improve localization accuracy and stability for defect regions of varying scales. Experimental results demonstrate that YOLO-DGW achieves an F1-score of 63.06% and an AP@0.50 of 62.07%, representing improvements of approximately 7.41% and 2.8%, respectively, over the strongest baseline method. Compared with several mainstream object detection models, the proposed approach exhibits superior performance in both detection accuracy and cross-region generalization capability. These findings indicate that integrating frequency-domain information into spatial feature learning through a modulation mechanism can effectively enhance the model’s ability to discriminate weak-reflection anomalies, providing a novel time-frequency collaborative modeling paradigm for railway GPR defect detection. Full article

Urban ecological vulnerability has become an important perspective for understanding ecosystem stability under environmental change. However, its spatiotemporal dynamics and driving mechanisms remain insufficiently understood in high-latitude grassland cities. This study focuses on the central urban area of Hailar and examines how ecological vulnerability evolves and what factors shape its spatial differentiation. Using the sensitivity–resilience–pressure (SRP) framework, a multidimensional evaluation system was constructed based on statistical yearbooks and GIS-based spatial data. Ecological vulnerability was assessed on a 1 km grid from 2010 to 2020, and its evolution was analyzed in three stages. The spatial pattern remains relatively stable but shows increasing differentiation over time. High-vulnerability areas are persistently concentrated in built-up regions, while low-vulnerability areas are mainly located in surrounding forest and grassland ecosystems with higher ecological resilience. Over time, vulnerability gradually shifts outward from the urban core, with clear intensification along the urban fringe. The results indicate that ecological vulnerability is driven by the interaction of sensitivity, resilience, and pressure, while urban expansion plays a key role in intensifying ecological stress and reshaping spatial patterns. The study provides a framework for understanding ecological vulnerability dynamics in high-latitude resource-based grassland cities and supports zoning-based ecological management and land-use optimization. Full article

Background/Objectives: Natamycin is an effective antifungal limited by poor solubility. This study aimed to develop and characterize natamycin-loaded liposomal vesicles as a biocompatible delivery system to improve stability and achieve controlled release for potential topical application in the treatment of fungal infections. Methods: Formulations were prepared using two phospholipid mixtures (Lipoid S100 and Phospholipon 90H) via standard (thin-film) and proliposome methods. Evaluation included encapsulation efficiency (EE%), particle size, zeta potential, the polydispersity index (PDI), and rheological properties. In vitro release kinetics were compared to a natamycin solution. Antifungal efficacy was tested against four Candida strains to determine minimum inhibitory and fungicidal concentrations (MICs and MFCs, respectively) and biofilm inhibition, while biocompatibility was assessed via keratinocyte viability assays. Results: Formulations achieved high encapsulation (~90%). Natamycin incorporation improved homogeneity and reduced particle diameters, particularly in proliposome-derived vesicles, suggesting strong drug–lipid interactions. Preparation method and lipid type significantly influenced properties; thin-film formulations showed a lower PDI and higher stability. Diffusion was twofold slower than the control, with Lipoid S100 proliposomes providing the most sustained release. The liposomes demonstrated robust antifungal activity (MICs: 0.00625–0.2 mg/mL) and effective biofilm inhibition against C. krusei. While high concentrations moderately reduced keratinocyte viability, lower doses remained biocompatible and slightly stimulatory. Conclusions: Lipid composition and preparation methods have minimal impact on the physical properties and in vitro release profiles of natamycin liposomes. These vesicles provide a dose-dependent, biocompatible platform for the controlled delivery of antifungals, showing significant in vitro inhibitory activity against Candida growth and biofilm formation. Full article